METHOD AND MACHINE LEARNING AGENT FOR EXECUTING MACHINE LEARNING IN AN EDGE CLOUD

Method and machine learning agent for executing machine learning on an industrial process by using computing resources in an edge cloud. A state of the industrial process is identified (2:1) and a learning model comprising a training algorithm for the machine learning is selected (2:2) based on the identified state. The training algorithm in the selected model is then adapted (2:4) so that the amount of available computing resources in the edge cloud is sufficient for computations in the training algorithm. The adapted training algorithm is finally applied (2:5) on data generated in the industrial process using computing resources in the edge cloud. Thereby, computing resources in the edge cloud can be used and no additional resources are needed, thus reducing latency and bandwidth consumption.